SELECTED APPLICATIONS OF DEEP NEURAL NETWORKS IN SKIN LESION DIAGNOSTIC
The article provides an overview of selected applications of deep neural networks in the diagnosis of skin lesions from human dermatoscopic images, including many dermatological diseases, including very dangerous malignant melanoma. The lesion segmentation process, features selection and classification was described. Application examples of binary and multiclass classification are given. The described algorithms have been widely used in the diagnosis of skin lesions. The effectiveness, specificity, and accuracy of classifiers were compared and analysed based on available datasets.
dermatoscopic images; neural networks; melanoma; skin lesions
Adegun A., Viriri S.: Deep learning-based system for automatic melanoma detection. IEEE Access 8/2020, 7160-7172. DOI: https://doi.org/10.1109/ACCESS.2019.2962812
Alendar F. et al.: Clear definitions,simple terminology, no metaphoric terms. Expert RevDermatol 3/2008, 27–29. DOI: https://doi.org/10.1586/17469818.104.22.168
Argenziano G. et al.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Archives of Dermatology 134/1998, 1563–1570. DOI: https://doi.org/10.1001/archderm.134.12.1563
Argenziano G. et al.: Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. Journal of American Academy of Dermatology 48(5)/2003, 679–693.
Attia M. et al.: Skin melanoma segmentation using recurrent and convolutional neural networks. Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium, IEEE, 292–296. DOI: https://doi.org/10.1109/ISBI.2017.7950522
Blum H., Ellwanger U.: Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions, British Journal of Dermatology 151(5)/2004, 1029–1038. DOI: https://doi.org/10.1111/j.1365-2133.2004.06210.x
Brinker T.J. et al.: Deep learning outperformed 136 of 157 dermatologists in a head-to-head der moscopic melanoma image classification task. Eur J Cancer 113/2019, 47–54.
Codella N. et al.: Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. Machine Learning in Medical Imaging 2015, 118–126. DOI: https://doi.org/10.1007/978-3-319-24888-2_15
Codella N. et al.: Deep learning ensembles for melanoma recognition in dermoscopy images 2016, http://arxiv.org/abs/1610.04662 (accessed 4 October 2019).
Esteva A. et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542/2017, 115–118.
Esteva A.: Dermatologist-level classification of skin cancer with deep neural networks. Nat. Res. 542(7639)/2017, 115–118,. DOI: https://doi.org/10.1038/nature21056
Ge Y. et al.: Melanoma seg-mentation and classification in clinical images using deep learning. ICMLC 2018: Proceedings of the 2018 10th International Conference on Machine Learning and Computing 2018, 252–256. DOI: https://doi.org/10.1145/3195106.3195164
Ge Z. et al.: Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images. Springer, Cham LNCS 10435/2017, 250–258. DOI: https://doi.org/10.1007/978-3-319-66179-7_29
Haenssle H.A. et al.: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 29/2018, 1836–1342. DOI: https://doi.org/10.1093/annonc/mdy520
Haenssle H.A. et al.: Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermato-logists working under less artificial conditions. Ann Oncol 31/2020, 137–143.
He K., Zhang X, Ren, S., Sun J.: Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition 2016, 770–778. DOI: https://doi.org/10.1109/CVPR.2016.90
Hekler A. et al.: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. Eur J Cancer 118/2019, 91–96. DOI: https://doi.org/10.1016/j.ejca.2019.06.012
Kittler H. et al.: Dermatoscopy of unpigmented lesions of the skin: A new classification of vessel morphology based on pattern analysis. Dermatopathology: Practical & Conceptual 14(4)/2008, 3.
Li Y., Shen L.: Skin lesion analysis towards melanoma detection using deep learning network. Sensors 18/2018, 556. DOI: https://doi.org/10.3390/s18020556
Lopez A. R. et al.: Skin lesion classification from dermatoscopic images using deep learning techniques.
Maia L. et al.: Evaluation of melanoma diagnosis using deep features, 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP), 1–4, 2018. DOI: https://doi.org/10.1109/IWSSIP.2018.8439373
Marchetti M. A. et al.: Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge: comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. J Am Acad Dermatol 78/2018, 270–277. DOI: https://doi.org/10.1016/j.jaad.2017.08.016
Marchetti M.A. et al: Computer algorithms show potential for improving dermatologists’ accuracy to diagnose cutaneous melanoma: results of the international skin imaging collaboration 2017. J Am Acad Dermatol 82/2020, 622–627. DOI: https://doi.org/10.1016/j.jaad.2019.07.016
Maron R.C. et al.: Systematic outperformance of 112 dermato-logists in multiclass skin cancer image classification by convo-lutional neural networks. Eur J Cancer 119/2019, 57–65.
Menzies S. et al.: Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features. Archives of Dermatology 132/1996, 1178–1182. DOI: https://doi.org/10.1001/archderm.132.10.1178
Murphree D. H. et al.: Deep learning for dermatologists: Part I. J Am Acad Dermatol 2020, 1–9.
Nida N. et al.: Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. International Journal of Medical Informatics 124/2019, 37–48. DOI: https://doi.org/10.1016/j.ijmedinf.2019.01.005
Nijeweme-d’Hollosy W. et al.: Evaluation of three machine learning models for self-referral decision support on low back pain in primary care. Int. J. Med. Inform. 110/2018, 31–41. DOI: https://doi.org/10.1016/j.ijmedinf.2017.11.010
Phillips M. et al.: Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions. JAMA Netw Open 2/2019, 1913436. DOI: https://doi.org/10.1001/jamanetworkopen.2019.13436
Rosendahl C., Cameron A., McColl I., Wilkinson I.: Dermatoscopy in routine practice, Chaos and Clues. Australian Family Physician 41(7)/2012.
Simonyan K., Zisserman A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
Szegedy C. et al.: Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition 2015, 1–9. DOI: https://doi.org/10.1109/CVPR.2015.7298594
Tschandl P et al.: Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, webbased, international, diagnostic study. Lancet Oncol 2019(20)/2019, 938–947. DOI: https://doi.org/10.1016/S1470-2045(19)30333-X
Wang Y. et al.: Incorporating clinical knowledge with constrained classifier chain into a multimodal deep network for melanoma detection. Computers in Biology and Medicine 137/2021, 104812. DOI: https://doi.org/10.1016/j.compbiomed.2021.104812
Young A. T. et al.: Artificial intelligence in dermatology: A Primer. Journal of Investigative Dermatology 140/2020, 1504–1512. DOI: https://doi.org/10.1016/j.jid.2020.02.026
Yu L. et al.: Automated melano-ma recognition in dermoscopy images via very deep residual networks. IEEE transactions on medical imaging 36(4)/2017, 994–1004. DOI: https://doi.org/10.1109/TMI.2016.2642839
Zhang J. et al.: Skin lesion classification in dermoscopy images using synergic deep learning, Springer Nature Switzerland, LNCS 11071/2018, 12–20. DOI: https://doi.org/10.1007/978-3-030-00934-2_2
Zhang X.: Melanoma segmentation based on deep learning. Computer Assisted Surgery 22/2017, 267–277. DOI: https://doi.org/10.1080/24699322.2017.1389405
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